Environmental variables modeling based on remote sensing data using time series analysis: forestry and agricultural applications

s = 7 and 8 TeV in the Compact Muon Solenoid experiment at the LHC, using data samples corresponding to integrated luminosities of up to 5.1 fb − 1 at 7 TeV and 5.3 fb − 1 at 8 TeV.

Variation of the residual WFE at the LGS Objective image plane (after removal of tip-tilt and focus) as a function of Zenith angle.. The temporal average of the

The first one is the Generalized Differential Image Motion Monitor (GDIMM), 1 aiming at monitoring all the integrated parameters of the optical turbulence (seeing, isoplanatic

(2018) describe at least six different hedging methods based on autoregressive conditionally heteroscedastic (ARCH) models, which seek to obtain an explanation of the

From that moment on, more proposals emerged focused on offering more efficient algorithms capable of processing larger sets of time series: the Proximity Forest algorithm [LSP +

(1991) “Remote sensing and geographical information systems as applied to mountain hazard analysis and environmental

The four established EO groups of concepts focus on the physical properties of optical and microwave remote sensing (WG4), remote sensing sensors and platforms, as well as

Using ground-based data, meteorological observations, and atmospheric environmental monitoring data, a comparative analysis of the microphysical and optical properties, and